Many recent advances in robotic manipulation have come through imitation learning,
yet these rely largely on mimicking a particularly hard-to-acquire form of demonstrations:
those collected on the same robot in the same room with the same objects
as the trained policy must handle at test time.
In contrast, large pre-recorded human video datasets
demonstrating manipulation skills in-the-wild already exist,
which contain valuable information for robots.
Is it possible to distill a repository of useful robotic skill policies out of such data
without any additional requirements on robot-specific demonstrations or exploration?
We present the first such system ZeroMimic,
that generates immediately deployable image goal-conditioned skill policies
for several common categories of manipulation tasks
(opening, closing, pouring, pick&place, cutting, and stirring)
each capable of acting upon diverse objects and across diverse unseen task setups.
ZeroMimic is carefully designed to exploit recent advances
in semantic and geometric visual understanding of human videos,
together with modern grasp affordance detectors and imitation policy classes.
After training ZeroMimic on the popular EpicKitchens dataset of ego-centric human videos,
we evaluate its out-of-the-box performance in varied kitchen settings,
demonstrating its impressive abilities to handle these varied tasks.
To enable plug-and-play reuse of ZeroMimic policies on other task setups and robots,
we will release software and policy checkpoints for all skills.
